This paper presents a novel methodology for the automated creation of 3D city models for Mexican cities using exclusively open data. In Mexico, while national topographic and elevation datasets exist, they lack crucial features like individual building footprints and road polygons, making it difficult to create 3D city models using the most common existing methodologies. The proposed method addresses these limitations by generating building footprints directly from high-resolution DSMs using a region-growing algorithm and deriving road polygons from the empty spaces between city blocks in the topographic data. These generated features, along with existing data for plant cover and water bodies, are then lifted to 3D using customisable rules. The methodology was implemented with Python and C++ scripts and tested in central Mexico City. Results show that the generated building footprints are often more accurate than those in global datasets (Microsoft, Google), particularly for non-rectilinear buildings, leading to recognisable city landmarks. However, the method has limitations, including missing approximately 30% of smaller buildings and occasionally misclassifying tall vegetation as buildings. Despite this, the work demonstrates the feasibility of creating useful 3D city models for the areas in Mexico with high-resolution elevation data.

Despite growing use of 3D city models (3DCMs) and urban digital twins (UDTs), web tools for their processing and visualization remain scarce. We present an interoperable, high-performance web application composed of a 3D tiler and a WebGPU viewer that enables scalable conversion, streaming, and rendering of urban datasets in compliance with open standards. The proposed system allows users to explore large-scale 3DCMs interactively without local installations. A showcase visualizing quality-validation results for a 3DCM demonstrates practical value. Experiments confirm that 3D Tiles 1.1 standard enables scalable data management and richer interaction, whereas WebGPU offers up to 7x better rendering performance on modern hardware. By presenting this solution and usage example, we aim to foster development of next-generation web-based 3D geospatial, digital-twin, and metaverse solutions.

BIM models of buildings are increasingly being created, and they can be used as a geometrically detailed and semantically rich source for GIS building models without the need for additional data acquisition. However, the existing level of detail (LoD) schemes for buildings are based on models created from very different sources, e.g. 2D topography and remote sensing measurements. In this paper, we propose four novel Levels of Detail (LoDs) specifically tailored for BIM-derived 3D building models. The proposed LoDs—LoDa, LoDb, LoDc, and LoDd offer abstractions that leverage BIM’s strengths while mitigating its limitations. LoDa provides a multi-surface representation of the footprint and roof, whereas LoDb, LoDc, and LoDd offer volumetric alternatives that better capture complex facades, vertical variations, and overhangs. The performance of these new LoDs was evaluated against the established LoD framework by Biljecki et al. (2016) using metrics such as area, volume, and spatial deviation. Results demonstrate that the proposed LoDs, particularly LoDa, LoDb, and the refined variants LoDc.2 and LoDd.2, can achieve a closer geometric approximation to the source model than standard LoD2.2, thereby enhancing the usability of BIM data in GIS applications like urban planning and building permit checks.

Building information modelling (BIM) and geoinformation are widely recognised as complementary sources of data. Whereas a BIM model can represent a single building or infrastructure project in high detail, geoinformation-based sources can represent different types of features in a large region with less detail. Integrating geoinformation and BIM is very useful in practice and constitutes an active research field—often referred to as GeoBIM. A short list of GeoBIM applications include: performing checks for the issuance of building permits using buildings (BIM) and city regulations (Geo), navigation that combines outdoor (Geo) and indoor (BIM) portions, facility management for infrastructure sites (BIM) that include the regional connections between the sites (Geo), and risk management using regional simulations (Geo) that also takes into account the impact on specific sites (BIM). [...]

The widespread use of three-dimensional (3D) city data plays a significant role in various applications, such as mixed reality, infrastructure facility management, solar potential analysis, navigation, and so on. Ensuring high spatial and semantic quality in these endeavours is crucial to gathering proper results. Ensuring quality means verifying that the data adheres to relevant standards. Although these relevant standards are openly published, there are issues with the names of interoperability and reusability in academic studies and software development efforts. In this study, these issues are addressed using semantic web technologies. Most 3D city models (3DCMs) are treated as knowledge graphs (KG) with this approach. The main contribution of the study is a web-based interoperable tool for validation of CityGML Level of Detail 2 (LOD2) 3DCMs, which is compatible with relevant standards. Besides, an open-source 3DCM-to-KG converter and an open validation ontology are published as by-products while accomplishing the main goal. By virtue of the KG approach, the 3DCM KG becomes capable of carrying its own validation constraints, which come from the validation ontology. With these efforts, this study provides a practical, interoperable solution to improve the quality and usability of 3DCMs and validation plans, fostering consistency across applications while aligning with established standards in the field.

Urban morphological analytics on buildings is informative for sustainable development. 3D building massing features, such as courtyards and setbacks, reflect spatial organizations and circulations, while influence daylight access, ventilation, and shading. However, existing 3D GIS methods usually overlook such 3D massing features, further obscure morphological analytics and environmental assessment. This article proposes MorphCut, an efficient convex decomposition method that segments 3D shapes into mass-aligned parts. MorphCut leverages key morphological properties—planarity, regularity, and Gestalt laws—after a topological preprocessing step to enable mass-aware decomposition. Experiments on representative samples, ranging from small houses to complex skyscrapers, showed that MorphCut outperformed four baseline methods in (i) balancing convexity and compactness, (ii) aligning decomposed parts with building masses, and (iii) preserving geometric fidelity (average deviation: 0.25 m). An urban-scale validation on datasets from Delft and Hong Kong, comprising over 30,000 buildings across 18.3 km², demonstrated MorphCut’s robustness, scalability, and generalizability. MorphCut successfully decomposed 98% of buildings in low-rise regions (+78% over the second-best method) and 93% in high-rise areas (+2%), completing processing in 13 hours (3 hours faster). These results position MorphCut as a foundational 3D GIS tool for large-scale, mass-aware morphological analysis, with implications for digital twins, sustainable planning, and environmental modeling.

This paper performs, describes, and evaluates a comparison of seven software tools (ArcGIS Pro, GRASS GIS, SAGA GIS, CitySim, Ladybug, SimStadt, and UMEP) to calculate solar irradiation. The analysis focuses on data requirements, software usability, and accuracy simulation output. The use case for the comparison is solar irradiation on building surfaces, in particular on roofs. The research involves collecting and preparing spatial and weather data. Two test areas—the Santana district in São Paulo, Brazil, and the Heino rural area in Raalte, the Netherlands—were selected. In both cases, the study area encompasses the vicinity of a weather station. Therefore, the meteorological data from these stations serve as ground truth for the validation of the simulation results. We create several models (raster and vector) to meet the diverse input requirements. We present our findings and discuss the output from the software tools from both quantitative and qualitative points of view. Vector-based simulation models offer better results than raster-based ones. However, they have more complex data requirements. Future research will focus on evaluating the quality of the simulation results on vertical and tilted surfaces as well as the calculation of direct and diffuse solar irradiation values for vector-based methods.

This paper presents an implemented methodology to convert highly detailed building information models (BIMs) into geospatial 3D city models (Geos) at multiple levels of detail (LoDs). As BIM models contain highly detailed and complex geometries that differ significantly from city model standards, abstraction and conversion methods are required to generate usable outputs. Our study addresses this by developing a methodology that generates nine different LoDs from a single IFC input. These LoDs include both volumetric and surface-based abstractions for exterior and interior representations. The methodology involves voxelisation, filtering and simplification of surfaces, footprint derivation, storey abstraction, and interior geometry extraction. Together, these approaches allow flexible conversion tailored to specific applications, balancing accuracy, complexity, and computational efficiency. The methodology is implemented in a prototype tool named IfcEnvelopeExtractor. It automates IFC-to-CityGML/CityJSON conversion with minimal user input. The methodology was tested on a variety of models ranging from small houses to multistorey buildings. The evaluation covered geometric accuracy, semantic accuracy, and model complexity. Results show that non-volumetric abstractions and interior abstractions performed very well, producing robust and accurate results. However, the accuracy decreased for volumetric and complex abstractions, particularly at higher LoDs. Problems included missing or incorrectly trimmed surfaces, and modelling gaps and tolerance issues in the input IFC models. These limitations reveal that the quality of the input BIM models significantly affects the reliability of conversions. Overall, the methodology demonstrates that automated, flexible, and open-source solutions can effectively bridge the gap between BIM and geospatial domains, contributing to scalable GeoBIM integration in practice.

In response to peer review feedback, the article underwent several key revisions to enhance clarity and academic rigour. First, the authors incorporated new and relevant literature, including a 2024 study on digital building permits and logbooks, as well as a 2023 paper on digital sustainability in horticulture. These additions aim to strengthen the theoretical foundation and contextual relevance of the study. To address concerns about theoretical depth, the Introduction was revised to provide a more precise explanation of the study’s contribution: a replicable method for mapping digital construction practices to global sustainability targets and identifying DBP and DBL practices that advance sustainable construction and building management. The Discussion and Conclusions sections were expanded to emphasise the unique contribution of the study, namely, the first systematic mapping of Digital Building Permit (DBP) and Digital Building Logbook (DBL) practices to specific UN Sustainable Development Goals (SDGs). Methodological transparency was improved by detailing the purposive sampling of experts, the rationale for the four-phase research design, and the tools used (Slido and Miro) for data collection and validation. Clarifications were added regarding the qualitative nature of the study, the absence of data normalisation, and the anonymisation of workshop responses. Language and formatting were also refined. Grammatical errors were corrected, long sentences shortened, and citation formatting reviewed. The figure and the table were verified for proper citation. Finally, the revised Conclusions section now explicitly acknowledges the study’s limitations, including its European focus, the use of single-point-in-time data collection, and the qualitative nature of its findings.

High-precision 3D urban applications — including emergency response simulation, microclimate analysis, and heritage conservation— demand semantically enriched 3D building representations at Level of Detail 3 (LoD3) with parametric façade components. Current urban digital twins predominantly rely on LoD2 models (as exemplified by the nationwide 3D BAG dataset in the Netherlands) that lack critical architectural features such as windows and doors, constraining their analytical value and their utility for fine-grained applications. This study introduces a novel pipeline to bridge this gap, enabling the enrichment of LoD2 models with accurate opening information using aerial oblique imagery and deep learning. The approach addresses critical challenges in 3D-2D alignment by leveraging perspective projection for comprehensive façade extraction, least-squares registration to rectify systematic offsets, and Mask R-CNN for robust opening detection. Unlike conventional methods, it captures both inward and outward building faces by projecting all 3D façades onto multi-directional images, ensuring complete coverage of visible elements. Geometric scaling integrates detected openings into LoD2 models as watertight, semantically rich components, validated for structural consistency. By overcoming data misalignments and occlusion limitations, this methodology provides a scalable framework for large-scale LoD3 generation, enabling efficient upgrades of existing building models to support detailed spatial analysis in smart city contexts. [...]

Data-driven prediction of infrastructure aging is challenging due to the complex stochastic nature of degradation effects and the ill-documented historical records. Degradation modeling is, however, crucial for predictive maintenance that is key for infrastructure integrity. This study presents a multi-attribute, data-driven approach for modelling stochastic degradation and maintenance effects of roads, mining an extensive database of geo-located historical inspection and maintenance records from the municipality of Amsterdam. Inspection data track pavement conditions at irregular intervals across ten discrete states per road segment, following the Dutch CROW 146 protocol. Damage severity and extent for eight damage modes is captured, i.e., for transverse unevenness, irregularities, ravelling, edge damage, crack formation, joint filling, joint width, and settling. The maintenance dataset includes >25k minor interventions across 17k road segments, indicating repair requirements, and 200+ major maintenance projects, covering 21k segments where interventions are planned, all without verifying completion. This complicates accurate modelling of natural degradation as it is confounded by maintenance effects. To address the issue of irregular inspections, degradation is first modelled as a continuous-time Markov chain. Thereby, transition rates are estimated, which are then converted to discrete-time Markov chain transition probability matrices to eventually support regular maintenance planning. We further learn the effects of minor and major maintenance activities, as defined and recorded in the database. Based on the estimated degradation transitions, pre-maintenance and post-maintenance state distributions are estimated. Instantaneous maintenance transition matrices are computed by minimizing the cross-entropy between the pre-maintenance state after the intervention and the post-maintenance state. The model allows for a multi-attribute approach, segmenting roads based on construction material (e.g., asphalt, tiled pavement) and traffic loads (e.g., residential, commercial/pedestrian). The approach is exemplified for tiled pavements for a section of the road network of Amsterdam, where the effects of minor and major maintenance are ablated for long-term predictions. Although applied to Amsterdam, this method is relevant to any infrastructure system with discrete state datasets, providing a foundation for data-driven decision-making in infrastructure management.

This paper presents the lessons learnt from the integration of open datasets in the Netherlands for the creation of a country-wide enriched semantic 3D city model for urban building energy modelling. Although the Netherlands provides open access to building data up to the dwelling level, several challenges still remain related to data fragmentation, inconsistency, and incompleteness. The resulting dataset uses the CityGML with the Energy ADE data model since they offer a robust framework for integrating geospatial and non-geospatial data for energy applications. Our research highlights the need for significant preprocessing, harmonisation pipelines, and enrichment strategies to address gaps in data completeness and reliability. Finally, we identify critical missing data (e.g., renovation history, thermal zoning, and detailed HVAC specifications) and propose directions for improvement.

In the abstract, there is a typo in the name of the study area in the Netherlands: It is “Henio,” it should be “Heino.” [...]

Solar energy is becoming increasingly important with the transition towards green and sustainable energy. Predicting solar irradiance is one of the core steps to optimise solar energy utilisation when planning and scheduling power grids. Accurate solar irradiance prediction can also help forecast microclimate conditions, enabling the analysis of citizens and planning of optimal intervention strategies for heating or cooling behaviour. This paper discusses a novel approach to computing the solar potential of buildings at the city level with promising scalability using semantic 3D city models. Experiments are conducted at different locations in the Netherlands. We evaluate our results by comparing them to the statistical Dutch data, and CitySim shows huge discrepancies in summer.

This paper presents a new algorithm for filling holes in Level of Detail 2 (LoD2) building mesh models, addressing the challenges posed by geometric inaccuracies and topological errors. Unlike traditional methods that often alter the original geometric structure or impose stringent input requirements, our approach preserves the integrity of the original model while effectively managing a range of topological errors. The algorithm operates in three distinct phases: (1) pre-processing, which addresses topological errors and identifies pseudo-holes; (2) detecting and extracting complete border rings of holes; and (3) remeshing, aimed at reconstructing the complete geometric surface. Our method demonstrates superior performance compared to related work in filling holes in building mesh models, achieving both uniform local geometry around the holes and structural completeness. Comparative experiments with established methods demonstrate our algorithm’s effectiveness in delivering more complete and geometrically consistent hole-filling results, albeit with a slight trade-off in efficiency. The paper also identifies challenges in handling certain complex scenarios and outlines future directions for research, including the pursuit of a comprehensive repair goal for LoD2 models to achieve watertight 2-manifold models with correctly oriented normals. Our source code is available at https://github.com/tudelft3d/Automatic-Repair-of-LoD2-Building-Models.git

Although level of detail (LoD) is a central concept in 3D city modelling, specifying different LoDs in an unambiguous manner is not straightforward. To resolve this, a set of frameworks have been developed. This paper evaluates the suitability of the LoD framework of (Biljecki et al. 2016) for 3D building models that have been generated directly from BIM models. The output of two BIM shell extractors are tested on how well they can be defined by the framework. It was found that although BIM-derived models can be specified by the framework to a certain degree, the framework is not fully capable to also specify lower quality models and to support all the output that may come from BIM shell extractors. This can be resolved by either addressing issues in the shell extractors’ output or in the framework itself. The results of this research can be used to improve the LoD framework and to adjust the shell extractors output to better comply with unambiguous definitions of building models at different LoDs and could be a first step to standardise the conversion of BIM models at different LoDs to be used in urban applications.

Delivering value from digital concepts such as Digital Twins is necessary to address systemic national and global issues, such as achieving Net Zero. However, there is still a lack of consensus over what a Digital Twin (DT) is and efforts to clarify this do not consider the Geospatial perspective. With the aspiration for national- and international-scale DTs, it is important that the Geospatial community understands its role in supporting the realisation of the value of these DTs. Here, a systematic literature review is used to gather DT case studies that use, or are inferred to use, elements of the Geospatial discipline. A total of 77 DT case studies about smart cities, manufacturing, energy, construction and agriculture are reviewed in full, and 24 Geospatial DT dimensions are defined and then compared with existing DT dimensions. The results indicate a considerable use of Geospatial Science in DTs that is not explicitly stated, meaning that there are possibly missed opportunities for collaboration between the Geospatial and DT communities. We conclude that the role of Geospatial Science in DTs is larger than stated and needs to be understood further.

This paper describes a data driven urban canopy model that can be coupled with detailed building energy models. The data driven model is used to assess the outdoor air temperature and humidity in a street canyon considering as inputs weather conditions at the atmospheric layer, the surface temperature of surrounding building facades and the street, and the heat released from the use of air-conditioning. Predictions made by the model were tested using measurements of the outdoor air temperature and humidity collected between April and August 2019 in Singapore. Results show that the model estimates the outdoor air temperature with a similar accuracy than others that were validated using the same input and test data, while providing estimates with a higher temporal resolution and considering urban morphology with a higher fidelity. They also demonstrate that the model can predict the impact of waste heat releases and cool pavement on the outdoor air temperature and building energy consumption. In the future, vegetation could be considered as an input of the model if the land surface temperature is measured using an infrared thermal camera. Another improvement would be to define weather conditions at the atmospheric layer from rooftop measurements or a climate model.

This paper suggests a method to simulate interactions between buildings and their outdoor conditions at the city-scale using a coupled scheme whose physical parameters are entirely assessed from data of the indoor and outdoor built environment. The coupled scheme consists of a reduced order building energy model and a single layer urban canopy model. In a previous study, it was proven that physical parameters of a single layer urban canopy model can be assessed using measurements of the outdoor temperature and humidity in a street canyon. For the coupled scheme to be fully data driven, the next step is to demonstrate that the reduced order building energy model can estimate the cooling consumption and exterior wall surface temperature in good agreement with measurements or simulated data after being trained using machine learning. Indeed, results show that a multi objective genetic algorithm can find values for physical parameters of the reduced order building energy model. Estimates of the cooling consumption and exterior wall surface temperature provided by the trained model achieve a CV-RMSE below 10% and a RMSE lower than 2.5 Kelvin, respectively, with respect to data generated from EnergyPlus. The last step towards a full data driven coupled scheme for city-scale simulations would be to iteratively train the reduce order building energy model with the single layer urban canopy model and show the convergence and accuracy of their respective outputs.

3D modeling of indoor spaces is a prerequisite for daylight simulation, and the accuracy of the 3D models has a significant impact on the simulation. The goal of this study was to quantify the errors caused by modeling indoor spaces at different accuracy levels to find the optimal balance between the reliability of the results and labor investment. For this purpose, we introduce a level of detail (LOD) concept for indoor spaces based on the size of non-permanent indoor objects by inclusion and exclusion from the simulation scene. The errors corresponding to models with low accuracies are measured by climate-based simulation using an improved two-phase method. Our results show that inaccurate modeling of indoor spaces causes between 10-70% error in TAI with 25% median across all spaces.